The Tissue Foreground Detection extension uses computer vision to detect where the tissue is within the image. This tool will generate a cell annotation corresponding to all of the true cells within the tissue foreground, rather than the background. This enables downstream analysis on true cells.

The underlying tissue detection model is trained in a supervised fashion on a set of DAPI images with various artifacts.

Configuring new runs

To configure a new run, specify

• Title for the resulting annotation set
• [Optional] Description for the resulting annotation set
• Regions to run the tissue foreground detection on
• Segmentation Version for which cells to create the annotation with
• Nuclear Biomarker to use to calculate where the tissue foreground is

Notes:

• This extension has been trained on DAPI. Results may vary for other biomarkers. Other high quality nuclear markers should retain similar performance.
• This extension may fail for particularly large images. We are working on an improvement, and you may email [email protected] in the meantime!
• Why aren’t all of my regions / segmentation versions appearing?

Viewing results

Once a run is complete, you should be able to select that run within the extension to open the results in the Visualizer.

Alternatively, you can go directly to the Visualizer to open the resulting annotation and view it.

The image above shows an example with tissue foreground cells in red and background in light blue.

If you have Workbench, you will additionally be able to leverage this annotation set in downstream analysis, to ensure you are analyzing only true, tissue foreground cells. You can also use Workbench to push this annotation set back to the web portal as a quality control filter using the function upload_qc (demonstrated in this SpatialMap tutorial). We are anticipating further improvements to our web portal to allow you to filter out non-cells directly within the web view! Stay tuned for more.

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